Organizations: Department of Physics, Indian Institute of Technology Kharagpur, 721302, West Bengal, India · Department of CSIS, BITS Pilani K K Birla Goa Campus (on lien to Center of AI and SuperComputing, Mahindra University, Hyderabad), Goa, 403726, India · Siebel School of Computing and Data Science, University of Illinois Urbana–Champaign, Champaign, IL 61801, USA · Indian Institute of Astrophysics, Koramangala, Bengaluru 560034, India · Observatoire de Paris, LERMA, College ’ de France, PSL University, Sorbonne University, CNRS, Paris, France-75014
Dual active galactic nuclei (DAGN) mark a critical phase in the evolution of merging galaxies and the pairing of supermassive black holes, yet they remain difficult to identify in large imaging surveys because of projection effects and limited spatial resolution. Compact foreground stars and unresolved substructure can mimic dual nuclei through chance superposition, complicating automated detection. We revisit the 46,061 galaxies flagged but rejected as DAGN candidates by the GOTHIC pipeline, primarily because the two nuclei fell within the SDSS fibre aperture or exceeded its separation threshold. We train a supervised deep-learning framework based on the YOLOv11 oriented-bounding-box architecture on annotated SDSS imaging to separate genuine dual nuclei from foreground stellar contaminants and other spurious alignments. The final model attains a validation precision of 0.919, recall of 0.905, and F1 of 0.912, and yields 29,605 dual-nucleus candidates after removing star-dominated and blended detections. Structured visual inspection indicates that 54.5--62% are consistent with genuine dual nuclei, implying ∼(1.4--1.8)×104 plausible systems. Cross-calibrating the YOLO separation against the deterministic GOTHIC centroid measurement and restricting to the compact regime (d≤6.87′′) gives a conservative subset of ∼13,672 candidates, reaching calibrated separations of ∼0.56′′. Spectroscopy of the most compact (≤1 kpc) systems shows spectra dominated by stellar absorption, with no resolved double-peaked emission, so confirmation requires higher-resolution follow-up. The catalogue is a statistically refined list of candidates, not confirmed DAGN. Nonetheless, deep-learning detection substantially reduces contamination and expands the plausible DAGN census.
Automated characterization of galactic substructure is an essential step in understanding the transformative physical processes driving galaxy evolution. In this study, we investigate the application of deep learning (DL) frameworks to characterize different galactic substructures hosted within parametric light-profile subtracted residual'' images of a large sample galaxies from the CANDELS survey. We develop a supervised Convolutional Neural Network (CNN) and unsupervised Convolutional Variational Autoencoder (CvAE) and train it on the single-Sérsic profile fitting based residual images of $10,046$ bright and massive galaxies ($H<24.5\,{\rm mag}$ and $M_{\rm stellar} \geq 10^{9.5}\,M_{\odot}$) spanning $1<z<3$, in conjunction with their visual-based classification labels indicating the nature of residual substructures hosted within them. Using our unique data preprocessing approach, we prepare our residual images such that the inputs to our DL networks comprise only galaxy of interest'', and augment them such that our sample span uniformly across different residual characteristics. We assess the latent space of the CNN and CvAE using Principle Component Analysis (PCA) along with independently quantified metrics of residual strength (significant pixel flux SPF, Bumpiness, and Residual Flux Fraction). We also employ an unsupervised Gaussian Mixture Modeling (GMM) based clustering scheme with Support Vector Classification (SVC) to identify groupings in PCA space that correspond to similar residual substructure. We find that our supervised CNN latent features in PCA space correlate with the SPF values and distinguish between qualitatively strong and weak residual substructures. While our unsupervised CvAE latent space also correlates with visual and quantitative residual characteristics, but lacks clear discriminatory power when characterizing different residual substructures.
Kameswara Bharadwaj Mantha, Daniel H. McIntosh, Cody Ciaschi +9
Department of Physics & Astronomy, University of Missouri Kansas City, Kansas City, MO, 64110, USA. · Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA. · Department of Physics, University of California, Santa Cruz, CA 95064, USA. +1
Integral Field Spectroscopy (IFS) surveys offer a unique new landscape in which to learn in both spatial and spectroscopic dimensions and could help uncover previously unknown insights into galaxy evolution. In this work, we demonstrate a new unsupervised deep learning framework using Convolutional Long-Short Term Memory Network Autoencoders to encode generalized feature representations across both spatial and spectroscopic dimensions spanning 19 optical emission lines (3800A <λ< 8000A) among a sample of ∼9000 galaxies from the MaNGA IFS survey. As a demonstrative exercise, we assess our model on a sample of 290 Active Galactic Nuclei (AGN) and highlight scientifically interesting characteristics of some highly anomalous AGN.
Department of Physics and Astronomy, University of Minnesota Twin Cities, Minneapolis, USA · Company Name, Location, Country · European Space Agency (ESA), European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n 28692 Villanueva de la Cañada, Madrid, Spain +4
We present a framework to cross-match sources from the Chandra Source Catalog (CSC v2.1) with optical sources from Gaia Data Release 3. Unlike purely spatial approaches, we use source properties such as magnitudes, colors, and distances to identify true counterparts, detect chance coincidences, and resolve ambiguities when multiple plausible candidates exist. We define a training set of high-confidence matches using NWAY, a Bayesian cross-matching framework that accounts for positional errors and source densities. We train a gradient-boosted classifier (LightGBM) on a variety of features from both catalogs. Of the ~254k unique X-ray sources, we find counterparts for ~113k sources, of which plausible multiple counterparts are found for ~7k. We find no counterparts for ~20k sources for which separation-based cross-matching does find a match, and attribute half of these to chance coincidences. We validate the pipeline on the Chandra Orion Ultradeep Project (COUP), where the machine-learning matches reproduce 95% of NWAY cross-matches without using any positional information. We release a catalog of the ~113k Chandra-Gaia counterparts, together with ~7k alternative matches and ~20k ambiguous NWAY associations, supporting future population studies of sources detectable by both Chandra and Gaia. We discuss limitations and provide a generalization of the framework that is applicable in other cross-matching scenarios.
V. Samuel Pérez-Díaz, Vinay L. Kashyap, Joshua D. Ingram +6
Center for Astrophysics | Harvard & Smithsonian, 60 Garden St, Cambridge MA 02138, USA · Harvard John A. Paulson School of Engineering and Applied Sciences, 150 Western Ave, Allston, MA 02134, USA · Universidad del Rosario, School of Engineering, Science and Technology, Cll. 12C No. 6-25, Bogot´a, Colombia +5